A Field Guide to Character Encodings

I recently gave a talk at PyCascades (a regional Python language conference) on character encodings and I thought it would be nice to put together a little primer on a couple different important character encodings.

So many characters, so little time.

If you’re unfamiliar with character encodings, they’re just a variety of different systems used to map a string of binary (i.e. 1’s and 0’s) to a specific character. So the Euro character, €, would be represented as “111000101000001010101100” in a character encoding called UTF-8 but “10100100” in the Latin 9 encoding. There are a lot of different character encodings out there, so I’m just going to cover a handful that I think are especially interesting/important.

Name: ASCII

Created: 1960

Also known as:American Standard Code for Information Interchange, US-ASCII

Most often seen: In legacy systems, especially U.S. government databases that need to be backward compatible

ASCII was the first widely-used character encoding. It has space for only 128 characters, and is best suited for English-language data.

Most often seen: Representing languages not covered by ASCII (like Spanish and Portuguese).

Latin 1 is the most popular of a large set of character encodings developed by the ISO (International Organization for Standardization) to deal with the fact that ASCII only really works well for English by adding additional characters. They did this by adding one extra bit to each character (8 instead of the 7 ASCII uses) so that they had space for 256 characters per encoding. However, since there are a lot of characters out there, there are 16 different ISO encodings that can handle different alphabets. (For example, ISO 8859-5 handles Cyrillic characters, while ISO 8859-11 maps Thai characters.)

While the ISO was developing a standard set of character encodings, pretty much every large software company was also developing thier own set of proprietary encodings that did pretty much the same thing. Windows-1252 is slightly tweaked version of Latin 1, but Windows also had a bunch of different encodings, as did Apple, as did IBM. The 1980’s were a wild time for character encodings!

Name: Shift-JIS

Created: 1997

Also known as: Shift Japanese Industrial Standards, SJIS, Shift_JIS

Most often seen: For Japanese

So one thing you may notice about the character encodings above is that they’re all fairly small, i.e. can’t handle more than 256 characters. But what about a language that has way more than 256 characters, like Japanese? (Japanese has a phonetic writing system with 71 characters, as well as 85,000 kanji characters which each represent a single word.) Well, one solution is to create a different character encoding system specifically for that language with enough space for all the characters you’re going to want to use frequently. But, like with the ASCII-based encodings I talked about above, just one encoding isn’t quite enough to cover all the needs of the language, so a lot of variants and extensions popped up.

Which brings us to UTF-8, the current standard for text encoding. The UTF encodings map from binary to what are known as Unicode codepoints, and then those codepoints are mapped to characters. Why the whole “codepoints” thing in the middle? To help overcome the problems with language-specific encodings that were discussed above. There are over one million codepoints, of which a little over 130,000 have actually been assigned to specific characters. You can update which binary patterns map to which codepoints independently of which codepoints map to which characters. The large number of code points also means that UTF encodings are also pretty future-proof: we have space to add a lot of new characters before we run out. And, in case you’re wondering, there is a single body in charge of determining which code points map to which characters (including emoji!). It’s called the Unicode Consortium and anyone’s free to join.

Like I mentioned, there are lots of different character encodings out there, but knowing about these five and how they’re related will give you a good idea of the complexities of the character encodings landscape. If you’re curious to learn more, the Unicode Standard is a good place to start.